Causal analysis of water MD simulations shows translational motions drive orientational dynamics in supercooled HDL but remain decoupled at ambient conditions, revealing an emergent arrow of time in fluctuation couplings.
Nosé, A unified formulation of the constant temperature molecular dy- namics methods, The Journal of Chemical Physics 81 (1984) 511–519
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Integrating MD simulations, physically derived scattering models, Bayesian model selection and polarisation analysis allows QENS to resolve anisotropic rotational diffusion in liquid benzene with stronger anisotropy than prior models indicated.
Machine learning force field molecular dynamics simulations reveal anisotropic crystallization in Sb2S3 with [100] facet fastest growth and interface-controlled kinetics with activation energy 0.55-0.57 eV.
The TAHM method approximates temperature-dependent conductivity by thermally averaging the square of the density of states near the Fermi level obtained from ab initio MD simulations on five test systems.
Fine-tuned MACE MLIPs achieve lower mean absolute errors on catalytic reaction energies and barriers than from-scratch models, with a large fine-tuned model performing best on both metallic and oxide systems including out-of-distribution cases.
Systematic benchmarking finds Grønbech-Jensen-Farago Langevin thermostat most consistent for temperature and energy sampling in binary LJ glass simulations, at roughly double the cost and with friction-dependent diffusion.
MACE-MPA-0 predicts Li diffusion Ea of 0.22 eV in LiF, fine-tuned version with 300 points gives 0.20 eV, close to DeePMD reference of 0.24 eV, using far less training data.
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Causality in Liquid Water as a Hallmark of Emergent Glassy Dynamics
Causal analysis of water MD simulations shows translational motions drive orientational dynamics in supercooled HDL but remain decoupled at ambient conditions, revealing an emergent arrow of time in fluctuation couplings.
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Lost in Translation: Simulation-Informed Bayesian Inference Improves Understanding of Molecular Motion From Neutron Scattering
Integrating MD simulations, physically derived scattering models, Bayesian model selection and polarisation analysis allows QENS to resolve anisotropic rotational diffusion in liquid benzene with stronger anisotropy than prior models indicated.
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Anisotropic Crystallization Kinetics and Interfacial Dynamics of Phase-Change Material Sb$_2$S$_3$ from Machine Learning Force Field Simulations
Machine learning force field molecular dynamics simulations reveal anisotropic crystallization in Sb2S3 with [100] facet fastest growth and interface-controlled kinetics with activation energy 0.55-0.57 eV.
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Temperature dependence of electronic conductivity from ab initio thermal simulation
The TAHM method approximates temperature-dependent conductivity by thermally averaging the square of the density of states near the Fermi level obtained from ab initio MD simulations on five test systems.
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Systematic Fine-Tuning of MACE Interatomic Potentials for Catalysis
Fine-tuned MACE MLIPs achieve lower mean absolute errors on catalytic reaction energies and barriers than from-scratch models, with a large fine-tuned model performing best on both metallic and oxide systems including out-of-distribution cases.
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Benchmarking thermostat algorithms in molecular dynamics simulations of a binary Lennard-Jones glass-former model
Systematic benchmarking finds Grønbech-Jensen-Farago Langevin thermostat most consistent for temperature and energy sampling in binary LJ glass simulations, at roughly double the cost and with friction-dependent diffusion.
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Comparing fine-tuning strategies of MACE machine learning force field for modeling Li-ion diffusion in LiF for batteries
MACE-MPA-0 predicts Li diffusion Ea of 0.22 eV in LiF, fine-tuned version with 300 points gives 0.20 eV, close to DeePMD reference of 0.24 eV, using far less training data.